Time to first recurrence as a trial endpoint: time to change?

Rahul Mehra1

  • 1Medtronic Inc., Minneapolis, MN 55432, USA. Rahul.Mehra@Medtronic.com

Cardiac Electrophysiology Review
|January 24, 2004
PubMed

Insights

Developing new atrial fibrillation therapies requires surrogate endpoints. "Time to first symptomatic recurrence" may not accurately reflect quality of life due to episode clustering, necessitating larger trials or alternative measures.

Area of Science:

  • Cardiology
  • Clinical Trials
  • Biostatistics

Background:

  • Interest in novel atrial fibrillation (AF) therapies is growing.
  • Evaluating therapeutic efficacy typically requires large trials measuring outcomes like mortality or quality of life.
  • Development of surrogate endpoints is crucial for reducing trial sample sizes.

Purpose of the Study:

  • To assess if "time to first recurrence of symptomatic atrial fibrillation" is an appropriate surrogate endpoint for quality of life.
  • To evaluate the validity of assumptions linking "time to first recurrence" to quality of life via episode frequency.

Main Methods:

  • Review of existing assumptions regarding surrogate endpoints for AF management.
  • Analysis of recent data from patients with implantable devices monitoring atrial tachyarrhythmias.
  • Comparison of observed episode patterns with the Poisson distribution model.

Main Results:

  • The assumption that "time to first recurrence" accurately reflects quality of life is questioned.
  • Atrial tachyarrhythmia episodes, including symptomatic ones, tend to cluster, deviating from a Poisson distribution.
  • Non-Poisson distributions necessitate larger sample sizes for detecting treatment differences in clinical trials.

Conclusions:

  • "Time to first symptomatic recurrence" may not be a reliable surrogate endpoint for quality of life in AF management.
  • Consideration of non-Poisson distributions is vital for designing future AF clinical trials.
  • Alternative surrogate endpoints, such as episode frequency, severity, duration, or objective rate control measures, should be explored.

Related Concept Videos

Clinical Trials01:16

Clinical Trials

Clinical trials are prospective experimental studies conducted on humans to determine the safety and efficacy of treatments, drugs, diet methods, and medical devices. Using statistics in clinical trials enables researchers to derive reasonable and accurate conclusions from the collected data, allowing them to make wise decisions in uncertain situations. In medical research, statistical methods are crucial for preventing errors and bias.
There are four phases in a clinical trial. A phase one...
Clinical Trials: Overview01:11

Clinical Trials: Overview

Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
Kaplan-Meier Approach01:24

Kaplan-Meier Approach

The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.